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source("tianfengRwrappers.R")

载入需要的程辑包：dplyr

载入程辑包：‘dplyr’

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union

载入需要的程辑包：reticulate
载入需要的程辑包：tidyr

载入程辑包：‘MySeuratWrappers’

The following objects are masked from ‘package:Seurat’:

    DimPlot, DoHeatmap, LabelClusters, RidgePlot, VlnPlot


载入程辑包：‘cowplot’

The following object is masked from ‘package:ggpubr’:

    get_legend

载入需要的程辑包：viridisLite

载入程辑包：‘reshape2’

The following object is masked from ‘package:tidyr’:

    smiths

NOTE: Either Arial Narrow or Roboto Condensed fonts are required to use these themes.
      Please use hrbrthemes::import_roboto_condensed() to install Roboto Condensed and
      if Arial Narrow is not on your system, please see https://bit.ly/arialnarrow

Registered S3 method overwritten by 'enrichplot':
  method               from
  fortify.enrichResult DOSE
clusterProfiler v3.14.3  For help: https://guangchuangyu.github.io/software/clusterProfiler

If you use clusterProfiler in published research, please cite:
Guangchuang Yu, Li-Gen Wang, Yanyan Han, Qing-Yu He. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS: A Journal of Integrative Biology. 2012, 16(5):284-287.
Registering fonts with R

载入程辑包：‘plotly’

The following object is masked from ‘package:ggplot2’:

    last_plot

The following object is masked from ‘package:stats’:

    filter

The following object is masked from ‘package:graphics’:

    layout

载入需要的程辑包：Biobase
载入需要的程辑包：BiocGenerics
载入需要的程辑包：parallel

载入程辑包：‘BiocGenerics’

The following objects are masked from ‘package:parallel’:

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ, clusterExport, clusterMap,
    parApply, parCapply, parLapply, parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from ‘package:dplyr’:

    combine, intersect, setdiff, union

The following objects are masked from ‘package:stats’:

    IQR, mad, sd, var, xtabs

The following objects are masked from ‘package:base’:

    anyDuplicated, append, as.data.frame, basename, cbind, colnames, dirname, do.call,
    duplicated, eval, evalq, Filter, Find, get, grep, grepl, intersect, is.unsorted,
    lapply, Map, mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
    Position, rank, rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit, which, which.max, which.min

Welcome to Bioconductor

    Vignettes contain introductory material; view with 'browseVignettes()'. To cite
    Bioconductor, see 'citation("Biobase")', and for packages 'citation("pkgname")'.

载入需要的程辑包：e1071

载入程辑包：‘widgetTools’

The following object is masked from ‘package:dplyr’:

    funs


载入程辑包：‘DynDoc’

The following object is masked from ‘package:BiocGenerics’:

    path


载入程辑包：‘DT’

The following object is masked from ‘package:Seurat’:

    JS

========================================
circlize version 0.4.13
CRAN page: https://cran.r-project.org/package=circlize
Github page: https://github.com/jokergoo/circlize
Documentation: https://jokergoo.github.io/circlize_book/book/

If you use it in published research, please cite:
Gu, Z. circlize implements and enhances circular visualization
  in R. Bioinformatics 2014.

This message can be suppressed by:
  suppressPackageStartupMessages(library(circlize))
========================================

载入需要的程辑包：grid
========================================
ComplexHeatmap version 2.2.0
Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
Github page: https://github.com/jokergoo/ComplexHeatmap
Documentation: http://jokergoo.github.io/ComplexHeatmap-reference

If you use it in published research, please cite:
Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional 
  genomic data. Bioinformatics 2016.
========================================


载入程辑包：‘ComplexHeatmap’

The following object is masked from ‘package:plotly’:

    add_heatmap

ds2 <- readRDS("ds2.rds")

病变亚群的识别

LUM

sep = 0.3
surfaceplot2("LUM",ds2,x_seq = seq(-2,13, sep), y_seq = seq(2,17,sep))

surfaceplot2("ACTA2",ds2,x_seq = seq(-2,13, sep), y_seq = seq(2,17,sep))

LUM

sep = 0.3
surfaceplot2("BGN",ds2,x_seq = seq(-2,13, sep), y_seq = seq(2,17,sep))

surfaceplot2("FB_score1",ds2,x_seq = seq(-2,13, sep), y_seq = seq(2,17,sep),z_height = 2)

surfaceplot2("SMC_score1",ds2,x_seq = seq(-2,13, sep), y_seq = seq(2,17,sep),z_height = 2)

dd <- umapplot(ds2,label = F)+scale_y_continuous(limits = c(2,17), breaks = NULL) +
        scale_x_continuous(limits = c(-2,13), breaks = NULL)+ 
  guides(colour = guide_legend(override.aes = list(size = 5))) +
        theme(axis.line = element_blank())
ggsave("dd.png",device = png,height = 4,width = 6,plot= dd, bg = "transparent")

addmodulescore

geneset <- read.table("SMC")
ds2 <- AddModuleScore(ds2,features = geneset, name = 'SMC_score')

Warning: The following features are not present in the object: SELM, PRKCDBP, SEPW1, NGFRAP1, not searching for symbol synonyms

df <- FetchData(ds2_PA,vars = c("FB_score1","SMC_score1","BGN","LUM","UMAP_1","UMAP_2"))
# df <- arrange(df,FB_score1,by_group = F)
data <- cbind(df,index = 1:nrow(df),cluster = Idents(ds2_PA))

ggplot(data,aes(x=SMC_score1)) + geom_point(aes(y = BGN, color = cluster),alpha = 1) + geom_smooth(aes(y = BGN), color = "red") + theme_classic() + mytheme +scale_y_continuous(limits = c(1,5)) + scale_color_manual(values = colors_list)

`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Warning: Removed 663 rows containing non-finite values (stat_smooth).
Warning: Removed 663 rows containing missing values (geom_point).

ggplot(data,aes(x=FB_score1)) + geom_point(aes(y = LUM, color = cluster),alpha = 1) + geom_smooth(aes(y = BGN), color = "green") + theme_classic() + mytheme +scale_y_continuous(limits = c(1,5)) + scale_color_manual(values = colors_list)

`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Warning: Removed 663 rows containing non-finite values (stat_smooth).
Warning: Removed 4086 rows containing missing values (geom_point).

GSVA

f("CUI_TCF21_TARGETS_UP", label = F, ds2) +scale_colour_gradient(low="#1E90FF", high="#ff2121")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

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